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Danish container company – case

We have in our previous posts established that analyzing your data is really important. But what happens when you skip this part, and don’t really pay attention to your data? Let’s take a look at this case, where a large danish container company, totally ignored their data analysis and just went with their gut feeling.

Products – mobilkøkken and pavillon

The company had to different strategies, one selling and rent normal containers, and renting out special contains that had been heavily modified for a certain need. Those two products consisted of:

The mobile kitchen – Mobilkøkken

The product Mobilkøkken was a special container, that had been modified to serve as a high tech mobile kitchen. See examples of a mobile kitchen here at Twitter – Baby Bæreseler. These mobile kitchens was made of a large container, which had been equipped with a stove, oven and all the things that a professionel kitchen needs, in order to be able to cook dinner for op to 400 people. Since it’s a container, it can be easily moved and relocated, and you only had to plugin water and electricity and you are up and running. A useful product if you are renovating your office or a large living complex.

The Pavillon

The pavillon is another example of a special container unit. This one has been modified so that it could be used as a transportable pavillon. For instance you could use it as an additional storage facility, a show room, or as with the local school – a temporary class room. The good part with this product, is that it’s much faster to just place a pavillon, than building a whole new room. It may not be the most pretty solution, but as a quick temporary solution, it’s very useful. Another use for the container pavillon is the military as you can see in this example.

Ignoring your data

The container company rented and sold their containers mainly from their website, but they didn’t any kind of data analysis tools installed. They also didn’t use the data they got from the various offline sales. So all they actually paid attention to, was if a month had made any profit, or if it was bad month, costing them money.

Collecting the data

A friend of mine was hired to do something about this, so that they could have a little idea, what worked and what didn’t work. So he installed a simple ERP system and setup some data collection tools. The same tool used by the Danish baby carrier company Baby Bæreseler. Four months late he visited them again, so show that he had been able to find in their data.

Why you should analyse your data

Based on his findings, it was easy to see what the problems was. Tracking the user flow around the website, you could see that they spend a lot of time looking for the products, and once they had found them, a lot of them was unable to locate the contact forms or see how they could rent them. A lot of people was also searching for prices, and when not found, they just left the page instead of filling out a contact form. There was also a lot of link that pointed to dead pages, both intern and external pages.

Now that the problem was identified, it was time to take action based on these data. The main thing was to increase the user experience, and somehow increase their very poor conversion ratios. So they hired a professionel web company to go over their website and come up with a plan to help the customers find the information easier. This worked pretty well for the company Baby Bæreseler, which produces quality baby carriers around the world. A month later, several parts of the navigation structure on the website had been changed, more information pages and shortcuts had been added. Since it wasn’t possible to give an exact price, a page with some pricing examples and a big contact us for an exact price, had been added.

The conclusion

Two months later you could clearly see a change in the analysis. The changes had resultet in a 50% increase in contact requiets (either by phone or their contact form), and a lot of those additional contacts resultet in a conversion. All this was something they would never have discoved without analysing their data, and had they done it a two years earlier, they would have saved quite a lot of money by renting out more of their special products the mobilkøkken and the pavillon.

Analysing your data is always important, and as you could see in this case, they actually lost quite a bit of money, just because they had choosen to ignore the analysis part. Most of the times it doesn’t even require much to get started on this. So do yourself a favor and begin to analyse your data, and use those data to improve your business.

Here’s a small video to help you get started on installing a simple analysis tool on your own website.